7664 lines
233 KiB
Plaintext
7664 lines
233 KiB
Plaintext
{
|
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"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "-R1oqVQiSIJY"
|
||
},
|
||
"source": [
|
||
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n",
|
||
"<div class=\"align-center\">\n",
|
||
"<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
|
||
"<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n",
|
||
"<a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n",
|
||
"</div>\n",
|
||
"\n",
|
||
"To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://docs.unsloth.ai/get-started/installing-+-updating).\n",
|
||
"\n",
|
||
"You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
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||
"id": "CF1IOzEcSIJZ"
|
||
},
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"source": [
|
||
"### News"
|
||
]
|
||
},
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{
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||
"cell_type": "markdown",
|
||
"metadata": {
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||
"id": "RzaiBA2nSIJZ"
|
||
},
|
||
"source": [
|
||
"Unsloth now supports Text-to-Speech (TTS) models. Read our [guide here](https://docs.unsloth.ai/basics/text-to-speech-tts-fine-tuning).\n",
|
||
"\n",
|
||
"Read our **[Qwen3 Guide](https://docs.unsloth.ai/basics/qwen3-how-to-run-and-fine-tune)** and check out our new **[Dynamic 2.0](https://docs.unsloth.ai/basics/unsloth-dynamic-2.0-ggufs)** quants which outperforms other quantization methods!\n",
|
||
"\n",
|
||
"Visit our docs for all our [model uploads](https://docs.unsloth.ai/get-started/all-our-models) and [notebooks](https://docs.unsloth.ai/get-started/unsloth-notebooks).\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "A6xkJnPSSIJZ"
|
||
},
|
||
"source": [
|
||
"### Installation"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "q2RYy2uuSIJZ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"%%capture\n",
|
||
"import os\n",
|
||
"if \"COLAB_\" not in \"\".join(os.environ.keys()):\n",
|
||
" !pip install unsloth\n",
|
||
"else:\n",
|
||
" # Do this only in Colab notebooks! Otherwise use pip install unsloth\n",
|
||
" !pip install --no-deps bitsandbytes accelerate xformers==0.0.29.post3 peft trl triton cut_cross_entropy unsloth_zoo\n",
|
||
" !pip install sentencepiece protobuf \"datasets>=3.4.1\" huggingface_hub hf_transfer\n",
|
||
" !pip install --no-deps unsloth"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "lgqVQAzKSIJa"
|
||
},
|
||
"source": [
|
||
"### Unsloth"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 300,
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"outputId": "c36582a5-ee6c-483c-8a5b-9ec43430d2af"
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},
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"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
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||
"/home/alexander/GitHub/finetune/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||
" from .autonotebook import tqdm as notebook_tqdm\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
|
||
"==((====))== Unsloth 2025.6.2: Fast Llama patching. Transformers: 4.52.4.\n",
|
||
" \\\\ /| NVIDIA GeForce RTX 4090. Num GPUs = 1. Max memory: 23.508 GB. Platform: Linux.\n",
|
||
"O^O/ \\_/ \\ Torch: 2.7.0+cu126. CUDA: 8.9. CUDA Toolkit: 12.6. Triton: 3.3.0\n",
|
||
"\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.30. FA2 = False]\n",
|
||
" \"-____-\" Free license: http://github.com/unslothai/unsloth\n",
|
||
"Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth import FastLanguageModel\n",
|
||
"import torch\n",
|
||
"max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
|
||
"dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
|
||
"load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
|
||
"\n",
|
||
"# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
|
||
"fourbit_models = [\n",
|
||
" \"unsloth/mistral-7b-v0.3-bnb-4bit\", # New Mistral v3 2x faster!\n",
|
||
" \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
|
||
" \"unsloth/llama-3-8b-bnb-4bit\", # Llama-3 15 trillion tokens model 2x faster!\n",
|
||
" \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n",
|
||
" \"unsloth/llama-3-70b-bnb-4bit\",\n",
|
||
" \"unsloth/Phi-3-mini-4k-instruct\", # Phi-3 2x faster!\n",
|
||
" \"unsloth/Phi-3-medium-4k-instruct\",\n",
|
||
" \"unsloth/mistral-7b-bnb-4bit\",\n",
|
||
" \"unsloth/gemma-7b-bnb-4bit\", # Gemma 2.2x faster!\n",
|
||
"] # More models at https://huggingface.co/unsloth\n",
|
||
"\n",
|
||
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
|
||
" model_name = \"unsloth/Meta-Llama-3.1-8B\",\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" dtype = dtype,\n",
|
||
" load_in_4bit = load_in_4bit,\n",
|
||
" # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "SXd9bTZd1aaL"
|
||
},
|
||
"source": [
|
||
"We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 2,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "6bZsfBuZDeCL",
|
||
"outputId": "b2a0373b-a721-4170-d7d3-8e6f6a33b0d5"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth 2025.6.2 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"model = FastLanguageModel.get_peft_model(\n",
|
||
" model,\n",
|
||
" r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n",
|
||
" target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
|
||
" \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
|
||
" lora_alpha = 16,\n",
|
||
" lora_dropout = 0, # Supports any, but = 0 is optimized\n",
|
||
" bias = \"none\", # Supports any, but = \"none\" is optimized\n",
|
||
" # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n",
|
||
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n",
|
||
" random_state = 3407,\n",
|
||
" use_rslora = False, # We support rank stabilized LoRA\n",
|
||
" loftq_config = None, # And LoftQ\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "vITh0KVJ10qX"
|
||
},
|
||
"source": [
|
||
"<a name=\"Data\"></a>\n",
|
||
"### Data Prep\n",
|
||
"We now use the Alpaca dataset from [vicgalle](https://huggingface.co/datasets/vicgalle/alpaca-gpt4), which is a version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html) generated from GPT4. You can replace this code section with your own data prep."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 3,
|
||
"metadata": {
|
||
"colab": {
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||
"base_uri": "https://localhost:8080/",
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"height": 159,
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"referenced_widgets": [
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]
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||
},
|
||
"id": "HvOPfPnet76H",
|
||
"outputId": "8f60bbe3-d43c-441f-99b3-3bb658aadb8e"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Repo card metadata block was not found. Setting CardData to empty.\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['instruction', 'input', 'output']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from datasets import load_dataset\n",
|
||
"\n",
|
||
"dataset = load_dataset(path = \"/home/alexander/GitHub/finetune\", data_files=\"trainingsdata.jsonl\", split=\"train\")\n",
|
||
"print(dataset.column_names)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "xg4_dG-m0Cz4"
|
||
},
|
||
"source": [
|
||
"One issue is this dataset has multiple columns. For `Ollama` and `llama.cpp` to function like a custom `ChatGPT` Chatbot, we must only have 2 columns - an `instruction` and an `output` column."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "DTQR4jrDMcJf",
|
||
"outputId": "d8b592db-cdf2-49e6-990d-3a493b0770ce"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"['instruction', 'input', 'output']\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(dataset.column_names)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "MwEbRFl0Mf3E"
|
||
},
|
||
"source": [
|
||
"To solve this, we shall do the following:\n",
|
||
"* Merge all columns into 1 instruction prompt.\n",
|
||
"* Remember LLMs are text predictors, so we can customize the instruction to anything we like!\n",
|
||
"* Use the `to_sharegpt` function to do this column merging process!\n",
|
||
"\n",
|
||
"For example below in our [Titanic CSV finetuning notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb), we merged multiple columns in 1 prompt:\n",
|
||
"\n",
|
||
"<img src=\"https://raw.githubusercontent.com/unslothai/unsloth/nightly/images/Merge.png\" height=\"100\">"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "w61VJ7rQM8jT"
|
||
},
|
||
"source": [
|
||
"To merge multiple columns into 1, use `merged_prompt`.\n",
|
||
"* Enclose all columns in curly braces `{}`.\n",
|
||
"* Optional text must be enclused in `[[]]`. For example if the column \"Pclass\" is empty, the merging function will not show the text and skp this. This is useful for datasets with missing values.\n",
|
||
"* You can select every column, or a few!\n",
|
||
"* Select the output or target / prediction column in `output_column_name`. For the Alpaca dataset, this will be `output`.\n",
|
||
"\n",
|
||
"To make the finetune handle multiple turns (like in ChatGPT), we have to create a \"fake\" dataset with multiple turns - we use `conversation_extension` to randomnly select some conversations from the dataset, and pack them together into 1 conversation."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 293,
|
||
"referenced_widgets": [
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"4dac566fd3ee404a8308026caf52e165",
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"50ec7aa2658e4cdf878dbd4c98660072",
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"4f808dffe6c24927b1b87b1319ccb311",
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"a843939563ee45299fdd3f32986462c5",
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"b1e8d77d5bc34e99824c873e55cc49a2",
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"5edc00f7efce452e82b713120068d5ce",
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"916ab8f1faf14632bdb3dead624eb21f",
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"de5c470b9d654008bb0032b5aa4329ce",
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"602377d8635c49118036fbb6817b3cee",
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"e660f7079e4346e499da1ac196c96980",
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"fa70fc9faa8a4580bf44e07f65e4d092",
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"8061d66b27d548ea86d7ef10d2d784e8",
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"18b7f26e9cc74aa7a3f01b7af539c4ba",
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"25397a2b86ef492c95c76b595f288e0d",
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"a79fd1eef2c543f9bef6ff4c78b7b9ac",
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"231d1e98fa704113bb70a4aef84ac30f",
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"e70183b7122c440eb0ad820a34ecd0b1",
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"36412dbe8646468dbc51dbac72ac305c",
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"70bb803085a2457092d4642a3baf8f54",
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"14c60f02caa441b7a5ce09ffd770f5a9",
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"ea6a29aacd804453863970f0e4bfb4ba",
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"1e6693fba36444749c5190ae984b7761",
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"5e1f0774a45141149ba3a9a62e2d50f1"
|
||
]
|
||
},
|
||
"id": "jZxeGSeX0CR8",
|
||
"outputId": "6486785e-69cc-4434-bef0-f5360ab2ac1a"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Merging columns: 100%|██████████| 289/289 [00:00<00:00, 70671.28 examples/s]\n",
|
||
"Merging columns: 100%|██████████| 289/289 [00:00<00:00, 70671.28 examples/s]\n",
|
||
"Converting to ShareGPT: 100%|██████████| 289/289 [00:00<00:00, 63506.78 examples/s]\n",
|
||
"Flattening the indices: 100%|██████████| 289/289 [00:00<00:00, 96309.70 examples/s]\n",
|
||
"Flattening the indices: 100%|██████████| 289/289 [00:00<00:00, 85200.95 examples/s]\n",
|
||
"Flattening the indices: 100%|██████████| 289/289 [00:00<00:00, 90062.70 examples/s]\n",
|
||
"Extending conversations: 100%|██████████| 289/289 [00:00<00:00, 21785.26 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth import to_sharegpt\n",
|
||
"\n",
|
||
"dataset = to_sharegpt(\n",
|
||
" dataset,\n",
|
||
" merged_prompt=\"{instruction}[[\\nYour input is:\\n{input}]]\",\n",
|
||
" output_column_name=\"output\",\n",
|
||
" conversation_extension=3, # Select more to handle longer conversations\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "1Kh90vpD1jYJ"
|
||
},
|
||
"source": [
|
||
"Finally use `standardize_sharegpt` to fix up the dataset!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 49,
|
||
"referenced_widgets": [
|
||
"d08ffe495f9649cd99fd13e38fa09aa5",
|
||
"e0a12ecf2f7b4af59ad6b9c6806cffd2",
|
||
"2748bc11780947038af15b94cdc0565a",
|
||
"39ac8df581254571bf15e52778564ad6",
|
||
"32f125cf43cd40a4b232d80a4cd0594f",
|
||
"0a3dd767c8284aa899195fb798a5da74",
|
||
"05e65597d7804e8eaa92d0df28a03b95",
|
||
"91b8e492c8e14eba9e9cd3c857e464ac",
|
||
"2342b7593577476ebc15b5b0b7c93d37",
|
||
"890767e37b054db5b6b2df4bb2ad351d",
|
||
"e9c0e9a6545c49b9895f751b6c738974"
|
||
]
|
||
},
|
||
"id": "ZPwDXBvP1g8S",
|
||
"outputId": "c400321c-0a47-47c3-d84c-e9ba524740a6"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth: Standardizing formats (num_proc=24): 100%|██████████| 289/289 [00:00<00:00, 2008.82 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from unsloth import standardize_sharegpt\n",
|
||
"\n",
|
||
"dataset = standardize_sharegpt(dataset)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "GThrcKACxTe2"
|
||
},
|
||
"source": [
|
||
"### Customizable Chat Templates\n",
|
||
"\n",
|
||
"You also need to specify a chat template. Previously, you could use the Alpaca format as shown below."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {
|
||
"id": "MVBanRIJRAcQ"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
|
||
"\n",
|
||
"### Instruction:\n",
|
||
"{}\n",
|
||
"\n",
|
||
"### Input:\n",
|
||
"{}\n",
|
||
"\n",
|
||
"### Response:\n",
|
||
"{}\"\"\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "VTzZ5oZrxkFz"
|
||
},
|
||
"source": [
|
||
"Now, you have to use `{INPUT}` for the instruction and `{OUTPUT}` for the response.\n",
|
||
"\n",
|
||
"We also allow you to use an optional `{SYSTEM}` field. This is useful for Ollama when you want to use a custom system prompt (also like in ChatGPT).\n",
|
||
"\n",
|
||
"You can also not put a `{SYSTEM}` field, and just put plain text.\n",
|
||
"\n",
|
||
"```python\n",
|
||
"chat_template = \"\"\"{SYSTEM}\n",
|
||
"USER: {INPUT}\n",
|
||
"ASSISTANT: {OUTPUT}\"\"\"\n",
|
||
"```\n",
|
||
"\n",
|
||
"Use below if you want to use the Llama-3 prompt format. You must use the `instruct` and not the `base` model if you use this!\n",
|
||
"```python\n",
|
||
"chat_template = \"\"\"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n",
|
||
"\n",
|
||
"{SYSTEM}<|eot_id|><|start_header_id|>user<|end_header_id|>\n",
|
||
"\n",
|
||
"{INPUT}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
|
||
"\n",
|
||
"{OUTPUT}<|eot_id|>\"\"\"\n",
|
||
"```\n",
|
||
"\n",
|
||
"For the ChatML format:\n",
|
||
"```python\n",
|
||
"chat_template = \"\"\"<|im_start|>system\n",
|
||
"{SYSTEM}<|im_end|>\n",
|
||
"<|im_start|>user\n",
|
||
"{INPUT}<|im_end|>\n",
|
||
"<|im_start|>assistant\n",
|
||
"{OUTPUT}<|im_end|>\"\"\"\n",
|
||
"```"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "EK-_ncj-RCNy"
|
||
},
|
||
"source": [
|
||
"The issue is the Alpaca format has 3 fields, whilst OpenAI style chatbots must only use 2 fields (instruction and response). That's why we used the `to_sharegpt` function to merge these columns into 1."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 67,
|
||
"referenced_widgets": [
|
||
"a1d70bbc33a64c23a63f773abc5e22e6",
|
||
"84f9e22160d74ec498f99e9c93b0c8bc",
|
||
"cb89bc91831b431bba8b1f1a14ac05ec",
|
||
"669649c93c60494fac2bc7dfb4b43ac1",
|
||
"c821f5cc39924fde9fc9d381747bc5d3",
|
||
"1ce9fb4fb360443fbab1bc426b84e51e",
|
||
"61e2f95ccaf84962a319224b652af287",
|
||
"cbf4c274668c4feeb407501754eb7bf1",
|
||
"9249cef6e8824525a1e183498d9d41ac",
|
||
"c37f06b87a9b41ba957ac760aad0ea48",
|
||
"9c9436e28d9c4cfdb9a251ce1ee4cb39"
|
||
]
|
||
},
|
||
"id": "JOGaZf1sdLlr",
|
||
"outputId": "ca525f00-aa3c-4563-af31-420f90e62acd"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth: We automatically added an EOS token to stop endless generations.\n",
|
||
"Map: 100%|██████████| 289/289 [00:00<00:00, 14356.40 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"chat_template = \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.\n",
|
||
"\n",
|
||
"### Instruction:\n",
|
||
"{INPUT}\n",
|
||
"\n",
|
||
"### Response:\n",
|
||
"{OUTPUT}\"\"\"\n",
|
||
"\n",
|
||
"from unsloth import apply_chat_template\n",
|
||
"\n",
|
||
"dataset = apply_chat_template(\n",
|
||
" dataset,\n",
|
||
" tokenizer=tokenizer,\n",
|
||
" chat_template=chat_template,\n",
|
||
" # default_system_message = \"You are a helpful assistant\", << [OPTIONAL]\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "idAEIeSQ3xdS"
|
||
},
|
||
"source": [
|
||
"<a name=\"Train\"></a>\n",
|
||
"### Train the model\n",
|
||
"Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 67,
|
||
"referenced_widgets": [
|
||
"dee51ee264e24b809be18f6d286ae874",
|
||
"a9a39684291d4eb1b7312640feae1462",
|
||
"d7435502d95f4355b37d2f3332b057f1",
|
||
"2d15261428824594908bd697644d61ed",
|
||
"b3a8a71922f341758e1adf0f951208bb",
|
||
"7f107e2a1d274a2f9bec4acce979d2da",
|
||
"0d5da7e017424aa0b7ff0d40abad1ecc",
|
||
"4c4bc99385eb49dd95ad4b740b949e55",
|
||
"f62d1a6521574459ae6fb1ebd71ad631",
|
||
"62191c10c14c4728bf85a293d29d37ce",
|
||
"ec56936c53984c85ade94df6ce443ad5"
|
||
]
|
||
},
|
||
"id": "95_Nn-89DhsL",
|
||
"outputId": "27f7cc9d-8fd9-4111-ee2d-a5a434b30282"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth: Tokenizing [\"text\"]: 100%|██████████| 289/289 [00:00<00:00, 2348.94 examples/s]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"from trl import SFTTrainer\n",
|
||
"from transformers import TrainingArguments\n",
|
||
"from unsloth import is_bfloat16_supported\n",
|
||
"\n",
|
||
"trainer = SFTTrainer(\n",
|
||
" model = model,\n",
|
||
" tokenizer = tokenizer,\n",
|
||
" train_dataset = dataset,\n",
|
||
" dataset_text_field = \"text\",\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" dataset_num_proc = 2,\n",
|
||
" packing = False, # Can make training 5x faster for short sequences.\n",
|
||
" args = TrainingArguments(\n",
|
||
" per_device_train_batch_size = 2,\n",
|
||
" gradient_accumulation_steps = 4,\n",
|
||
" warmup_steps = 5,\n",
|
||
" max_steps = 120,\n",
|
||
" # num_train_epochs = 1, # For longer training runs!\n",
|
||
" learning_rate = 2e-4,\n",
|
||
" fp16 = not is_bfloat16_supported(),\n",
|
||
" bf16 = is_bfloat16_supported(),\n",
|
||
" logging_steps = 1,\n",
|
||
" optim = \"adamw_8bit\",\n",
|
||
" weight_decay = 0.01,\n",
|
||
" lr_scheduler_type = \"linear\",\n",
|
||
" seed = 3407,\n",
|
||
" output_dir = \"outputs\",\n",
|
||
" report_to = \"none\", # Use this for WandB etc\n",
|
||
" ),\n",
|
||
")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {
|
||
"cellView": "form",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "2ejIt2xSNKKp",
|
||
"outputId": "623e558b-a1c9-425b-feff-4dcdbc381d0c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"GPU = NVIDIA GeForce RTX 4090. Max memory = 23.508 GB.\n",
|
||
"6.256 GB of memory reserved.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# @title Show current memory stats\n",
|
||
"gpu_stats = torch.cuda.get_device_properties(0)\n",
|
||
"start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
|
||
"max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n",
|
||
"print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n",
|
||
"print(f\"{start_gpu_memory} GB of memory reserved.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 1000
|
||
},
|
||
"id": "yqxqAZ7KJ4oL",
|
||
"outputId": "fd7d79c2-68a7-4e3a-e567-adecc3b1be87"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
|
||
" \\\\ /| Num examples = 289 | Num Epochs = 4 | Total steps = 120\n",
|
||
"O^O/ \\_/ \\ Batch size per device = 2 | Gradient accumulation steps = 4\n",
|
||
"\\ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8\n",
|
||
" \"-____-\" Trainable parameters = 41,943,040/8,000,000,000 (0.52% trained)\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Unsloth: Will smartly offload gradients to save VRAM!\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"\n",
|
||
" <div>\n",
|
||
" \n",
|
||
" <progress value='120' max='120' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
|
||
" [120/120 03:47, Epoch 3/4]\n",
|
||
" </div>\n",
|
||
" <table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: left;\">\n",
|
||
" <th>Step</th>\n",
|
||
" <th>Training Loss</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2.517900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>2</td>\n",
|
||
" <td>2.848100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>3</td>\n",
|
||
" <td>2.631700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>4</td>\n",
|
||
" <td>2.691000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>5</td>\n",
|
||
" <td>2.561800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>6</td>\n",
|
||
" <td>1.750800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>7</td>\n",
|
||
" <td>2.130300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>8</td>\n",
|
||
" <td>1.853600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>9</td>\n",
|
||
" <td>2.055000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>10</td>\n",
|
||
" <td>2.106600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>11</td>\n",
|
||
" <td>2.157200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>12</td>\n",
|
||
" <td>1.925300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>13</td>\n",
|
||
" <td>2.146500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>14</td>\n",
|
||
" <td>1.850600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>15</td>\n",
|
||
" <td>1.384100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>16</td>\n",
|
||
" <td>1.848100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>17</td>\n",
|
||
" <td>1.742400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>18</td>\n",
|
||
" <td>1.613100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>19</td>\n",
|
||
" <td>1.612900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>20</td>\n",
|
||
" <td>1.525700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>21</td>\n",
|
||
" <td>1.287500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>22</td>\n",
|
||
" <td>1.370300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>23</td>\n",
|
||
" <td>1.309100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>24</td>\n",
|
||
" <td>1.708700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>25</td>\n",
|
||
" <td>1.491900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>26</td>\n",
|
||
" <td>1.464000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>27</td>\n",
|
||
" <td>1.439800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>28</td>\n",
|
||
" <td>1.319100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>29</td>\n",
|
||
" <td>1.204800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>30</td>\n",
|
||
" <td>1.208500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>31</td>\n",
|
||
" <td>1.173300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>32</td>\n",
|
||
" <td>1.422600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>33</td>\n",
|
||
" <td>1.399800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>34</td>\n",
|
||
" <td>1.222500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>35</td>\n",
|
||
" <td>1.186000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>36</td>\n",
|
||
" <td>0.947800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>37</td>\n",
|
||
" <td>0.747600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>38</td>\n",
|
||
" <td>0.959000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>39</td>\n",
|
||
" <td>1.087000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>40</td>\n",
|
||
" <td>1.033500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>41</td>\n",
|
||
" <td>1.048900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>42</td>\n",
|
||
" <td>1.013500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>43</td>\n",
|
||
" <td>1.108400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>44</td>\n",
|
||
" <td>1.163500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>45</td>\n",
|
||
" <td>1.180400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>46</td>\n",
|
||
" <td>0.880200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>47</td>\n",
|
||
" <td>0.985200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>48</td>\n",
|
||
" <td>1.024300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>49</td>\n",
|
||
" <td>0.890800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>50</td>\n",
|
||
" <td>0.722400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>51</td>\n",
|
||
" <td>0.570700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>52</td>\n",
|
||
" <td>0.743300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>53</td>\n",
|
||
" <td>0.868600</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>54</td>\n",
|
||
" <td>0.906300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>55</td>\n",
|
||
" <td>0.863300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>56</td>\n",
|
||
" <td>0.813100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>57</td>\n",
|
||
" <td>0.807200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>58</td>\n",
|
||
" <td>0.551300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>59</td>\n",
|
||
" <td>0.651500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>60</td>\n",
|
||
" <td>0.945200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>61</td>\n",
|
||
" <td>0.532400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>62</td>\n",
|
||
" <td>0.767400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>63</td>\n",
|
||
" <td>0.735800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>64</td>\n",
|
||
" <td>0.762000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>65</td>\n",
|
||
" <td>0.824000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>66</td>\n",
|
||
" <td>0.683000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>67</td>\n",
|
||
" <td>0.737000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>68</td>\n",
|
||
" <td>0.499300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>69</td>\n",
|
||
" <td>0.685700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>70</td>\n",
|
||
" <td>0.534700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>71</td>\n",
|
||
" <td>0.665100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>72</td>\n",
|
||
" <td>0.617700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>73</td>\n",
|
||
" <td>0.630400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>74</td>\n",
|
||
" <td>0.829000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>75</td>\n",
|
||
" <td>0.357100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>76</td>\n",
|
||
" <td>0.499300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>77</td>\n",
|
||
" <td>0.430500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>78</td>\n",
|
||
" <td>0.433200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>79</td>\n",
|
||
" <td>0.461800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>80</td>\n",
|
||
" <td>0.579500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>81</td>\n",
|
||
" <td>0.402100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>82</td>\n",
|
||
" <td>0.426200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>83</td>\n",
|
||
" <td>0.302900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>84</td>\n",
|
||
" <td>0.383100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>85</td>\n",
|
||
" <td>0.469200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>86</td>\n",
|
||
" <td>0.361700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>87</td>\n",
|
||
" <td>0.272900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>88</td>\n",
|
||
" <td>0.370700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>89</td>\n",
|
||
" <td>0.403200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>90</td>\n",
|
||
" <td>0.319400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>91</td>\n",
|
||
" <td>0.572100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>92</td>\n",
|
||
" <td>0.384100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>93</td>\n",
|
||
" <td>0.413400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>94</td>\n",
|
||
" <td>0.505300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>95</td>\n",
|
||
" <td>0.320300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>96</td>\n",
|
||
" <td>0.544800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>97</td>\n",
|
||
" <td>0.483900</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>98</td>\n",
|
||
" <td>0.313100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>99</td>\n",
|
||
" <td>0.346300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>100</td>\n",
|
||
" <td>0.296300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>101</td>\n",
|
||
" <td>0.295300</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>102</td>\n",
|
||
" <td>0.385200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>103</td>\n",
|
||
" <td>0.538400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>104</td>\n",
|
||
" <td>0.318400</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>105</td>\n",
|
||
" <td>0.454500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>106</td>\n",
|
||
" <td>0.234100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>107</td>\n",
|
||
" <td>0.355500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>108</td>\n",
|
||
" <td>0.299000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>109</td>\n",
|
||
" <td>0.442100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>110</td>\n",
|
||
" <td>0.368000</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>111</td>\n",
|
||
" <td>0.308800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>112</td>\n",
|
||
" <td>0.282800</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>113</td>\n",
|
||
" <td>0.248500</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>114</td>\n",
|
||
" <td>0.271200</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>115</td>\n",
|
||
" <td>0.353700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>116</td>\n",
|
||
" <td>0.212100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>117</td>\n",
|
||
" <td>0.237100</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>118</td>\n",
|
||
" <td>0.335700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>119</td>\n",
|
||
" <td>0.272700</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <td>120</td>\n",
|
||
" <td>0.303200</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table><p>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.HTML object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"trainer_stats = trainer.train()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {
|
||
"cellView": "form",
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "pCqnaKmlO1U9",
|
||
"outputId": "38fed629-e855-4386-c150-8578e0a73a15"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"229.3242 seconds used for training.\n",
|
||
"3.82 minutes used for training.\n",
|
||
"Peak reserved memory = 7.113 GB.\n",
|
||
"Peak reserved memory for training = 0.857 GB.\n",
|
||
"Peak reserved memory % of max memory = 30.258 %.\n",
|
||
"Peak reserved memory for training % of max memory = 3.646 %.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# @title Show final memory and time stats\n",
|
||
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
|
||
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
|
||
"used_percentage = round(used_memory / max_memory * 100, 3)\n",
|
||
"lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
|
||
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
|
||
"print(\n",
|
||
" f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",
|
||
")\n",
|
||
"print(f\"Peak reserved memory = {used_memory} GB.\")\n",
|
||
"print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n",
|
||
"print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n",
|
||
"print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "ekOmTR1hSNcr"
|
||
},
|
||
"source": [
|
||
"<a name=\"Inference\"></a>\n",
|
||
"### Inference\n",
|
||
"Let's run the model! Unsloth makes inference natively 2x faster as well! You should use prompts which are similar to the ones you had finetuned on, otherwise you might get bad results!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "kR3gIAX-SM2q",
|
||
"outputId": "40b5e499-1ad5-4907-a284-9416a36d010c"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Die maximale Säumnisgebühr pro Medium beträgt 6,00 €.<|end_of_text|>\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"messages = [ # Change below!\n",
|
||
" {\"role\": \"user\", \"content\": \"Wie hoch ist die maximale Säumnisgebühr?\"},\n",
|
||
"]\n",
|
||
"input_ids = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" add_generation_prompt = True,\n",
|
||
" return_tensors = \"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"from transformers import TextStreamer\n",
|
||
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
|
||
"_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "CrSvZObor0lY"
|
||
},
|
||
"source": [
|
||
"Since we created an actual chatbot, you can also do longer conversations by manually adding alternating conversations between the user and assistant!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "JcbFUWEyQVaE",
|
||
"outputId": "517ed3fe-009e-4ebf-d233-2883e943de82"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"messages = [ # Change below!\n",
|
||
" {\"role\": \"user\", \"content\": \"Continue the fibonacci sequence! Your input is 1, 1, 2, 3, 5, 8\"},\n",
|
||
" {\"role\": \"assistant\", \"content\": \"The fibonacci sequence continues as 13, 21, 34, 55 and 89.\"},\n",
|
||
" {\"role\": \"user\", \"content\": \"What is France's tallest tower called?\"},\n",
|
||
"]\n",
|
||
"input_ids = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" add_generation_prompt = True,\n",
|
||
" return_tensors = \"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"from transformers import TextStreamer\n",
|
||
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
|
||
"_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "uMuVrWbjAzhc"
|
||
},
|
||
"source": [
|
||
"<a name=\"Save\"></a>\n",
|
||
"### Saving, loading finetuned models\n",
|
||
"To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
|
||
"\n",
|
||
"**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "upcOlWe7A1vc",
|
||
"outputId": "587e0389-0044-47a2-f691-581d262ea95c"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"model.save_pretrained(\"llama_lora_model\") # Local saving\n",
|
||
"tokenizer.save_pretrained(\"llama_lora_model\")\n",
|
||
"# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n",
|
||
"# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "AEEcJ4qfC7Lp"
|
||
},
|
||
"source": [
|
||
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "MKX_XKs_BNZR",
|
||
"outputId": "98ec2273-2e01-4062-c576-1ffb7b3afdb0"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if False:\n",
|
||
" from unsloth import FastLanguageModel\n",
|
||
" model, tokenizer = FastLanguageModel.from_pretrained(\n",
|
||
" model_name = \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
|
||
" max_seq_length = max_seq_length,\n",
|
||
" dtype = dtype,\n",
|
||
" load_in_4bit = load_in_4bit,\n",
|
||
" )\n",
|
||
" FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n",
|
||
"pass\n",
|
||
"\n",
|
||
"messages = [ # Change below!\n",
|
||
" {\"role\": \"user\", \"content\": \"Describe anything special about a sequence. Your input is 1, 1, 2, 3, 5, 8,\"},\n",
|
||
"]\n",
|
||
"input_ids = tokenizer.apply_chat_template(\n",
|
||
" messages,\n",
|
||
" add_generation_prompt = True,\n",
|
||
" return_tensors = \"pt\",\n",
|
||
").to(\"cuda\")\n",
|
||
"\n",
|
||
"from transformers import TextStreamer\n",
|
||
"text_streamer = TextStreamer(tokenizer, skip_prompt = True)\n",
|
||
"_ = model.generate(input_ids, streamer = text_streamer, max_new_tokens = 128, pad_token_id = tokenizer.eos_token_id)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "QQMjaNrjsU5_"
|
||
},
|
||
"source": [
|
||
"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "yFfaXG0WsQuE"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"if False:\n",
|
||
" # I highly do NOT suggest - use Unsloth if possible\n",
|
||
" from peft import AutoPeftModelForCausalLM\n",
|
||
" from transformers import AutoTokenizer\n",
|
||
" model = AutoPeftModelForCausalLM.from_pretrained(\n",
|
||
" \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n",
|
||
" load_in_4bit = load_in_4bit,\n",
|
||
" )\n",
|
||
" tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "XOFzC441vCtq"
|
||
},
|
||
"source": [
|
||
"<a name=\"Ollama\"></a>\n",
|
||
"### Ollama Support\n",
|
||
"\n",
|
||
"[Unsloth](https://github.com/unslothai/unsloth) now allows you to automatically finetune and create a [Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md), and export to [Ollama](https://ollama.com/)! This makes finetuning much easier and provides a seamless workflow from `Unsloth` to `Ollama`!\n",
|
||
"\n",
|
||
"Let's first install `Ollama`!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "NUxcyP_UfeLl",
|
||
"outputId": "69972ce0-9caf-41fd-b19a-fa058521990b"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!curl -fsSL https://ollama.com/install.sh | sh"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "TCv4vXHd61i7"
|
||
},
|
||
"source": [
|
||
"Next, we shall save the model to GGUF / llama.cpp\n",
|
||
"\n",
|
||
"We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n",
|
||
"\n",
|
||
"Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n",
|
||
"* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n",
|
||
"* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n",
|
||
"* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n",
|
||
"\n",
|
||
"We also support saving to multiple GGUF options in a list fashion! This can speed things up by 10 minutes or more if you want multiple export formats!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "FqfebeAdT073",
|
||
"outputId": "9d2292eb-1e31-4c88-9b44-5371e4104abf"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"# Save to 8bit Q8_0\n",
|
||
"if True: model.save_pretrained_gguf(\"model\", tokenizer,)\n",
|
||
"# Remember to go to https://huggingface.co/settings/tokens for a token!\n",
|
||
"# And change hf to your username!\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n",
|
||
"\n",
|
||
"# Save to 16bit GGUF\n",
|
||
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n",
|
||
"\n",
|
||
"# Save to q4_k_m GGUF\n",
|
||
"if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n",
|
||
"if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")\n",
|
||
"\n",
|
||
"# Save to multiple GGUF options - much faster if you want multiple!\n",
|
||
"if False:\n",
|
||
" model.push_to_hub_gguf(\n",
|
||
" \"hf/model\", # Change hf to your username!\n",
|
||
" tokenizer,\n",
|
||
" quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n",
|
||
" token = \"\", # Get a token at https://huggingface.co/settings/tokens\n",
|
||
" )"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "J7lk6l0CuPXS"
|
||
},
|
||
"source": [
|
||
"We use `subprocess` to start `Ollama` up in a non blocking fashion! In your own desktop, you can simply open up a new `terminal` and type `ollama serve`, but in Colab, we have to use this hack!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"id": "mcP9omF_tN7Q"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"import subprocess\n",
|
||
"\n",
|
||
"subprocess.Popen([\"ollama\", \"serve\"])\n",
|
||
"import time\n",
|
||
"\n",
|
||
"time.sleep(3) # Wait for a few seconds for Ollama to load!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "md3PExRLRhOc"
|
||
},
|
||
"source": [
|
||
"`Ollama` needs a `Modelfile`, which specifies the model's prompt format. Let's print Unsloth's auto generated one:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "h82vfNigRhiz",
|
||
"outputId": "bcd91437-d4cf-47de-8905-475e3fc4deec"
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"FROM {__FILE_LOCATION__}\n",
|
||
"\n",
|
||
"TEMPLATE \"\"\"Below are some instructions that describe some tasks. Write responses that appropriately complete each request.{{ if .Prompt }}\n",
|
||
"\n",
|
||
"### Instruction:\n",
|
||
"{{ .Prompt }}{{ end }}\n",
|
||
"\n",
|
||
"### Response:\n",
|
||
"{{ .Response }}<|end_of_text|>\"\"\"\n",
|
||
"\n",
|
||
"PARAMETER stop \"<|start_header_id|>\"\n",
|
||
"PARAMETER stop \"<|eot_id|>\"\n",
|
||
"PARAMETER stop \"<|python_tag|>\"\n",
|
||
"PARAMETER stop \"<|eom_id|>\"\n",
|
||
"PARAMETER stop \"<|finetune_right_pad_id|>\"\n",
|
||
"PARAMETER stop \"<|end_header_id|>\"\n",
|
||
"PARAMETER stop \"<|end_of_text|>\"\n",
|
||
"PARAMETER stop \"<|reserved_special_token_\"\n",
|
||
"PARAMETER temperature 1.5\n",
|
||
"PARAMETER min_p 0.1\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(tokenizer._ollama_modelfile)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "j6cipBJBudxv"
|
||
},
|
||
"source": [
|
||
"We now will create an `Ollama` model called `unsloth_model` using the `Modelfile` which we auto generated!"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "SDTUJv_QiaVh",
|
||
"outputId": "66fcae42-3792-4b52-eb42-d867d9f83d69"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!ollama create unsloth_model -f ./model/Modelfile"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "-KSoKTKQukba"
|
||
},
|
||
"source": [
|
||
"And now we can do inference on it via `Ollama`!\n",
|
||
"\n",
|
||
"You can also upload to `Ollama` and try the `Ollama` Desktop app by heading to https://www.ollama.com/"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "rkp0uMrNpYaW",
|
||
"outputId": "38bb3bd7-4a29-4c81-e319-388dcd96a449"
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"!curl http://localhost:11434/api/chat -d '{ \\\n",
|
||
" \"model\": \"unsloth_model\", \\\n",
|
||
" \"messages\": [ \\\n",
|
||
" { \"role\": \"user\", \"content\": \"Continue the Fibonacci sequence: 1, 1, 2, 3, 5, 8,\" } \\\n",
|
||
" ] \\\n",
|
||
" }'"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {
|
||
"id": "XnMbhp7KsKhr"
|
||
},
|
||
"source": [
|
||
"# ChatGPT interactive mode\n",
|
||
"\n",
|
||
"### ⭐ To run the finetuned model like in a ChatGPT style interface, first click the **| >_ |** button.\n",
|
||
"\n",
|
||
"\n",
|
||
"---\n",
|
||
"---\n",
|
||
"---\n",
|
||
"\n",
|
||
"### ⭐ Then, type `ollama run unsloth_model`\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"---\n",
|
||
"---\n",
|
||
"---\n",
|
||
"### ⭐ And you have a ChatGPT style assistant!\n",
|
||
"\n",
|
||
"### Type any question you like and press `ENTER`. If you want to exit, hit `CTRL + D`\n",
|
||
"You can also use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in llama.cpp or a UI based system like Jan or Open WebUI. You can install Jan [here](https://github.com/janhq/jan) and Open WebUI [here](https://github.com/open-webui/open-webui)\n",
|
||
"\n",
|
||
"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n",
|
||
"\n",
|
||
"Some other links:\n",
|
||
"1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n",
|
||
"2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n",
|
||
"3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n",
|
||
"6. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://docs.unsloth.ai/get-started/unsloth-notebooks)!\n",
|
||
"\n",
|
||
"<div class=\"align-center\">\n",
|
||
" <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n",
|
||
" <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n",
|
||
" <a href=\"https://docs.unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n",
|
||
"\n",
|
||
" Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n",
|
||
"</div>\n"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"accelerator": "GPU",
|
||
"colab": {
|
||
"gpuType": "T4",
|
||
"provenance": []
|
||
},
|
||
"kernelspec": {
|
||
"display_name": "finetune",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
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||
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||
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